class_docstr: Stack ensemble meta-model which functions identically to :class:`BaggedEnsembleModel` with the additional capability to leverage base models.
  
  By specifying base models during init, stacker models can use the base model predictions as features during training and inference.
  
  This property allows for significantly improved model quality in many situations compared to non-stacking alternatives.
  
  Stacker models can act as base models to other stacker models, enabling multi-layer stack ensembling.
code: def pred_probas_to_df(
      self, pred_proba: list, index=None
  ) -> pd.DataFrame:
      if self.problem_type in [MULTICLASS, SOFTCLASS, QUANTILE]:
          pred_proba = np.concatenate(pred_proba, axis=1)
          pred_proba = pd.DataFrame(
              pred_proba, columns=self.stack_columns
          )
      else:
          pred_proba = pd.DataFrame(
              data=np.asarray(pred_proba).T, columns=self.stack_columns
          )
      if index is not None:
          pred_proba.set_index(index, inplace=True)
      return pred_proba
  
imports: import copy
  import logging
  import os
  import time
  from collections import defaultdict
  from typing import Dict
  import numpy
  import pandas
  from autogluon.common.features.feature_metadata import FeatureMetadata
  from autogluon.common.features.types import R_FLOAT, S_STACK
  from autogluon.common.utils.path_converter import PathConverter
  from ...constants import MULTICLASS, QUANTILE, SOFTCLASS
  from ..abstract.abstract_model import AbstractModel
  from .bagged_ensemble_model import BaggedEnsembleModel
label_desc: Use image node_id 7 for calling the StackerEnsembleModel obj's underlying member method code with example usage: obj.pred_probas_to_df(pred_proba, index) and returns: pred_proba